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Description
Algorithmic advice has been shown to outperform human reasoning in various domains. However, prior research suggests that humans might be reluctant to accept it and proposed multiple avenues to increase the acceptance. To structure these approaches and potentially shed light on inconclusive results of prior studies, we propose a novel perspective on the acceptance of AI-based recommendations based on the elaboration likelihood model (ELM). This research in progress paper introduces our perspective on AI-based recommendations as persuasive messages, suggests the ELM as a promising approach to guide interventions aiming to increase their acceptance, and develops testable hypotheses to evaluate the model. We, thereby, include the moderating effects of individual and situational variables.
Recommended Citation
Michels, Leonard; Ochmann, Jessica; Tiefenbeck, Verena; and Laumer, Sven, "The Acceptance of AI-based Recommendations: An Elaboration Likelihood Perspective" (2022). Wirtschaftsinformatik 2022 Proceedings. 1.
https://aisel.aisnet.org/wi2022/ai/ai/1
The Acceptance of AI-based Recommendations: An Elaboration Likelihood Perspective
Algorithmic advice has been shown to outperform human reasoning in various domains. However, prior research suggests that humans might be reluctant to accept it and proposed multiple avenues to increase the acceptance. To structure these approaches and potentially shed light on inconclusive results of prior studies, we propose a novel perspective on the acceptance of AI-based recommendations based on the elaboration likelihood model (ELM). This research in progress paper introduces our perspective on AI-based recommendations as persuasive messages, suggests the ELM as a promising approach to guide interventions aiming to increase their acceptance, and develops testable hypotheses to evaluate the model. We, thereby, include the moderating effects of individual and situational variables.